




'''simple docstring'''


from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class      A       :
							lowercase_							     =							field(
							    metadata={'help': 'The output directory where the model will be written.'}	,)
							lowercase_							     =							field(
							    metadata={
							        'help': (
							            'The encoder model checkpoint for weights initialization.'
							            'Don\'t set if you want to train an encoder model from scratch.'
							        )
							    }	,)
							lowercase_							     =							field(
							    metadata={
							        'help': (
							            'The decoder model checkpoint for weights initialization.'
							            'Don\'t set if you want to train a decoder model from scratch.'
							        )
							    }	,)
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}       )
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}       )



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							HfArgumentParser((ModelArguments,) )
							((_a)	,				)      	=							parser.parse_args_into_dataclasses()

							# Load pretrained model and tokenizer

							# Use explicit specified encoder config
							if model_args.encoder_config_name:
														_a      	=							AutoConfig.from_pretrained(model_args.encoder_config_name )
							# Use pretrained encoder model's config
							else:
														_a      	=							AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )

							# Use explicit specified decoder config
							if model_args.decoder_config_name:
														_a      	=							AutoConfig.from_pretrained(model_args.decoder_config_name )
							# Use pretrained decoder model's config
							else:
														_a      	=							AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )

							# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
							_a      	=							True
							_a      	=							True

							_a      	=							FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
							    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path					,		decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path					,		encoder_config=UpperCamelCase					,		decoder_config=UpperCamelCase					,		)

							# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
							_a      	=							decoder_config.decoder_start_token_id
							_a      	=							decoder_config.pad_token_id
							if decoder_start_token_id is None:
														_a      	=							decoder_config.bos_token_id
							if pad_token_id is None:
														_a      	=							decoder_config.eos_token_id

							# This is necessary to make Flax's generate() work
							_a      	=							decoder_config.eos_token_id
							_a      	=							decoder_start_token_id
							_a      	=							pad_token_id

							_a      	=							AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )

							_a      	=							AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
							_a      	=							tokenizer.convert_ids_to_tokens(model.config.pad_token_id )

							model.save_pretrained(model_args.output_dir )
							image_processor.save_pretrained(model_args.output_dir )
							tokenizer.save_pretrained(model_args.output_dir )


if __name__ == "__main__":
				main()


"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  List[Any]    				=			'''Wav2Vec2FeatureExtractor'''
				A__ :  Any    				=			'''AutoTokenizer'''

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										super().__init__(_lowerCamelCase      ,					_lowerCamelCase )
										lowerCamelCase__   =self.feature_extractor
										lowerCamelCase__   =False

				@classmethod
				def 						_a      ( cls      ,					_lowerCamelCase      ,					**_lowerCamelCase ):
										try:
																return super().from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )
										except OSError:
																warnings.warn(
																    F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
																    " include a `tokenizer_class` attribute is deprecated and will be "
																    "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
																    " attribute to either your `config.json` or `tokenizer_config.json` "
																    "file to suppress this warning: "      ,					_lowerCamelCase      ,					)

																lowerCamelCase__   =WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )
																lowerCamelCase__   =WavaVecaCTCTokenizer.from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )

																return cls(feature_extractor=_lowerCamelCase      ,					tokenizer=_lowerCamelCase )

				def __call__( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										# For backward compatibility
										if self._in_target_context_manager:
																return self.current_processor(*_lowerCamelCase      ,					**_lowerCamelCase )

										if "raw_speech" in kwargs:
																warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
																lowerCamelCase__   =kwargs.pop("raw_speech" )
										else:
																lowerCamelCase__   =kwargs.pop("audio"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("sampling_rate"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("text"      ,					_lowerCamelCase )
										if len(_lowerCamelCase ) > 0:
																lowerCamelCase__   =args[0]
																lowerCamelCase__   =args[1:]

										if audio is None and text is None:
																raise ValueError("You need to specify either an `audio` or `text` input to process." )

										if audio is not None:
																lowerCamelCase__   =self.feature_extractor(_lowerCamelCase      ,					*_lowerCamelCase      ,					sampling_rate=_lowerCamelCase      ,					**_lowerCamelCase )
										if text is not None:
																lowerCamelCase__   =self.tokenizer(_lowerCamelCase      ,					**_lowerCamelCase )

										if text is None:
																return inputs
										elif audio is None:
																return encodings
										else:
																lowerCamelCase__   =encodings["input_ids"]
																return inputs

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										# For backward compatibility
										if self._in_target_context_manager:
																return self.current_processor.pad(*_lowerCamelCase      ,					**_lowerCamelCase )

										lowerCamelCase__   =kwargs.pop("input_features"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("labels"      ,					_lowerCamelCase )
										if len(_lowerCamelCase ) > 0:
																lowerCamelCase__   =args[0]
																lowerCamelCase__   =args[1:]

										if input_features is not None:
																lowerCamelCase__   =self.feature_extractor.pad(_lowerCamelCase      ,					*_lowerCamelCase      ,					**_lowerCamelCase )
										if labels is not None:
																lowerCamelCase__   =self.tokenizer.pad(_lowerCamelCase      ,					**_lowerCamelCase )

										if labels is None:
																return input_features
										elif input_features is None:
																return labels
										else:
																lowerCamelCase__   =labels["input_ids"]
																return input_features

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										return self.tokenizer.batch_decode(*_lowerCamelCase      ,					**_lowerCamelCase )

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										return self.tokenizer.decode(*_lowerCamelCase      ,					**_lowerCamelCase )






				@contextmanager
				def 						_a      ( self ):
										warnings.warn(
										    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
										    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
										    "your audio inputs, or in a separate call." )
										lowerCamelCase__   =True
										lowerCamelCase__   =self.tokenizer
										yield
										lowerCamelCase__   =self.feature_extractor
										lowerCamelCase__   =False


